Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram.
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ABSTRACT:
SUBMITTER: Chen D
PROVIDER: S-EPMC7794254 | biostudies-literature | 2021 Jan
REPOSITORIES: biostudies-literature
Publications
Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram.
Chen Dexin D Liu Zhangyuanzhu Z Liu Wenju W Fu Meiting M Jiang Wei W Xu Shuoyu S Wang Guangxing G Chen Feng F Lu Jianping J Chen Hao H Dong Xiaoyu X Li Guoxin G Chen Gang G Zhuo Shuangmu S Yan Jun J
Nature communications 20210108 1
Accurate prediction of peritoneal metastasis for gastric cancer (GC) with serosal invasion is crucial in clinic. The presence of collagen in the tumour microenvironment affects the metastasis of cancer cells. Herein, we propose a collagen signature, which is composed of multiple collagen features in the tumour microenvironment of the serosa derived from multiphoton imaging, to describe the extent of collagen alterations. We find that a high collagen signature is significantly associated with a h ...[more]
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Project description:BackgroundA different treatment was used when peritoneal metastases (PM) occurred in patients with gastric cancer (GC). Certain cancers' peritoneal metastasis could be predicted by the cardiophrenic angle lymph node (CALN). This study aimed to establish a predictive model for PM of gastric cancer based on the CALN.MethodsOur center retrospectively analyzed all GC patients between January 2017 and October 2019. Pre-surgery computed tomography (CT) scans were performed on all patients. The clinicopathological and CALN features were recorded. PM risk factors were identified via univariate and multivariate logistic regression analyses. The receiver operator characteristic (ROC) curves were generated using these CALN values. Using the calibration plot, the model fit was assessed. A decision curve analysis (DCA) was conducted to assess the clinical utility.Results126 of 483 (26.1%) patients were confirmed as having peritoneal metastasis. These relevant factors were associated with PM: age, sex, T stage, N stage, enlarged retroperitoneal lymph nodes (ERLN), CALN, the long diameter of the largest CALN (LD of LCALN), the short diameter of the largest CALN (SD of LCALN), and the number of CALNs (N of CALNs). The multivariate analysis illustrated that the LD of LCALN (OR = 2.752, p < 0.001) was PM's independent risk factor in GC patients. The area under the curve (AUC) of the model was 0.907 (95% CI 0.872-0.941), demonstrating good performance in the predictive value of PM. There is excellent calibration evident from the calibration plot, which is close to the diagonal. The DCA was presented for the nomogram.ConclusionCALN could predict gastric cancer peritoneal metastasis. The model in this study provided a powerful predictive tool for determining PM in GC patients and helping clinicians allocate treatment.
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Project description:ImportanceOccult peritoneal metastasis frequently occurs in patients with advanced gastric cancer and is poorly diagnosed with currently available tools. Because the presence of peritoneal metastasis precludes the possibility of curative surgery, there is an unmet need for a noninvasive approach to reliably identify patients with occult peritoneal metastasis.ObjectiveTo assess the use of a deep learning model for predicting occult peritoneal metastasis based on preoperative computed tomography images.Design, setting, and participantsIn this multicenter, retrospective cohort study, a deep convolutional neural network, the Peritoneal Metastasis Network (PMetNet), was trained to predict occult peritoneal metastasis based on preoperative computed tomography images. Data from a cohort of 1225 patients with gastric cancer who underwent surgery at Sun Yat-sen University Cancer Center (Guangzhou, China) were used for training purposes. To externally validate the model, data were collected from 2 independent cohorts comprising a total of 753 patients with gastric cancer who underwent surgery at Nanfang Hospital (Guangzhou, China) or the Third Affiliated Hospital of Southern Medical University (Guangzhou, China). The status of peritoneal metastasis for all patients was confirmed by pathological examination of pleural specimens obtained during surgery. Detailed clinicopathological data were collected for each patient. Data analysis was performed between September 1, 2019, and January 31, 2020.Main outcomes and measuresThe area under the receiver operating characteristic curve (AUC) and decision curve were analyzed to evaluate performance in predicting occult peritoneal metastasis.ResultsA total of 1978 patients (mean [SD] age, 56.0 [12.2] years; 1350 [68.3%] male) were included in the study. The PMetNet model achieved an AUC of 0.946 (95% CI, 0.927-0.965), with a sensitivity of 75.4% and a specificity of 92.9% in external validation cohort 1. In external validation cohort 2, the AUC was 0.920 (95% CI, 0.848-0.992), with a sensitivity of 87.5% and a specificity of 98.2%. The discrimination performance of PMetNet was substantially higher than conventional clinicopathological factors (AUC range, 0.51-0.63). In multivariable logistic regression analysis, PMetNet was an independent predictor of occult peritoneal metastasis.Conclusions and relevanceThe findings of this cohort study suggest that the PMetNet model can serve as a reliable noninvasive tool for early identification of patients with clinically occult peritoneal metastasis, which will inform individualized preoperative treatment decision-making and may avoid unnecessary surgery and complications. These results warrant further validation in prospective studies.
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